Deep reinforcement learning (DRL) has achieved remarkable success in a range of tasks, from real-world problems [1]} to games [2]}. Moreover, focus on a large scale has caused to solve some challenging games such as StartCraft 2 [3]} and Dota [4]}. While these successes have been considerable, the development has been mostly in single-task performance. Also, DRL heavily relied on trial-and-error and empirical evaluation, and need the agent to explore a lot that makes the time a bottleneck. Consequently, there is a significant demand for distributed approaches to advance this field.
